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Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification.

Wenli Tian1, Ming Li1, Xiangyu Ju1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

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|August 26, 2022
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Summary
This summary is machine-generated.

This study enhances EEG-based human identification by combining multiple functional connectivity (FC) features using graph convolutional neural networks (GCN). This approach achieves high accuracy and robustness, improving security system efficiency.

Keywords:
EEGGCNfeature fusionfunctional connectivityhuman identification

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biometrics

Background:

  • Increasing demand for security drives interest in EEG-based human identification.
  • Improving accuracy while avoiding overfitting with multiple features is a key challenge.

Purpose of the Study:

  • To investigate the effectiveness of combining multiple functional connectivity (FC) features for EEG-based human identification.
  • To leverage graph convolutional neural networks (GCN) to enhance classification accuracy and system robustness.

Main Methods:

  • Utilized graph convolutional neural networks (GCN) for classification.
  • Combined multiple functional connectivity (FC) features as the GCN's structure matrix.
  • Evaluated performance on a public dataset, assessing accuracy and sensitivity to channel reduction.

Main Results:

  • Achieved high classification accuracy of 98.56% by integrating multiple FC features.
  • Demonstrated reduced sensitivity to channel reduction compared to traditional methods.
  • Successfully applied GCN to combine diverse FC data for identity authentication.

Conclusions:

  • Combining multiple FC features with GCN significantly improves EEG-based human identification accuracy.
  • The proposed method offers robustness against channel reduction, potentially lowering system costs.
  • This approach provides a promising direction for more efficient and accurate biometric security systems.